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The effects of sea surface temperature (SST) on ocean radar backscattering are investigated under both the neutral and nonneutral atmospheric conditions for the applications of wind retrieval. The impact factors are parameterized as functions of SST. The SST effects on the variations in ocean scattering and wind retrieval are evaluated using an analytic model which combines the KHCC03 spectrum and the second-order small slope approximation (SSA-II) model. Under the neutral condition, we present the following new insights at three commonly used bands: 1) the seawater permittivity accounts for a dominant effect of SST at the L-band. The SST effects induce a wind underestimation of 0.3 m/s over cold seawater and a wind overestimation of 0.24 m/s over warm seawater at the L-band and a wind speed of 8 m/s. The seawater viscosity plays a significant role in the SST effects on ocean scattering at the C- and Ku-bands, while its variation induced by SST has insignificant effects on L-band scattering; 2) for the C-band, the SST-induced wind retrieval error can be neglected at a medium wind speed due to the neutralization of the effects of various factors on surface roughness. Yet, the SST effects are not negligible at low and high wind speeds; and 3) both the dielectric and dynamic factors play significant roles in the SST effects on ocean scattering at the Ku-band. Under the nonneutral condition, the simulation results show that the air–sea interaction governs the SST effects on ocean scattering and wind velocity variations. Other than the air–sea interaction, the wind retrieval errors induced by other SST-related factors are negligible at the L- and C-bands. [ABSTRACT FROM AUTHOR]

This brief developed a novel sparsity constrained recursive adaptive filtering algorithm via the generalized correntropy criterion with variable center (GMCC-VC) for sparse system parameters estimation. In order to improve the performance of the adaptive filtering algorithm with GMCC under the non-Gaussian and non-zero mean noise environments, the GMCC is extended by adding a center which can be located at any position in the generalized Gaussian kernel function. Specifically, we proposed a novel adaptive online update method to optimize the kernel method and center. In addition, we mainly derived a recursive GMCC-VC algorithm based on the GMCC with the sparse constraint to exploit sparsity. Numerical simulation results are given to show that the proposed algorithm is efficient for sparse system parameter estimation problems in non-Gaussian and non-mean noise environments. [ABSTRACT FROM AUTHOR]

The unscented Kalman filter (UKF) provides a powerful tool for power system forecasting-aided state estimation (FASE). However, when the power systems are affected by the abnormal operating situations, i.e., the non-Gaussian communication noises, sudden loads or state changes, and instrument failures, the original UKF based on the minimum mean square error (MMSE) criterion may suffer from performance degradation. In contrast to the MMSE criterion, the minimum error entropy (MEE) exhibits the robustness with respect to complex non-Gaussian disturbances. In this article, we develop a new unscented Kalman-type filter based on the MEE criterion, termed MEE-UKF. To derive the MEE-UKF, a statistical linearization approach is adopted in the augmented model such that the state and measurement errors are combined in the MEE cost function simultaneously. Then, a fixed-point iteration algorithm is used to recursively update the posterior estimates and covariance matrix. Apart from the impulsive noises, the MEE-UKF can deal with complex multimodal distribution noises in both process and measurement. The high accuracy and strong robustness of MEE-UKF are confirmed by the simulation results on IEEE 14, 30, and 57 bus test systems under different non-Gaussian disturbances. [ABSTRACT FROM AUTHOR]

Due to the existence of various anomalies such as non-Gaussian process and measurement noises, gross measurement errors, and sudden changes of system status, the robust forecasting-aided state estimation is pivotal for power system stability. This paper develops a novel unscented Kalman filter (UKF) with the generalized correntropy loss (GCL) (termed as GCL-UKF) to estimate power system state with forecasting aid. The GCL is used to replace the mean square error loss in the original UKF framework. The advantage of such an approach is that it combines the strength of the GCL developed in robust information theoretic learning for addressing the non-Gaussian interference and the strength of the UKF in handling strong model nonlinearities. In addition, we take into account the nontrivial influences of the bad data for the innovation vector. An enhanced GCL-UKF method is established by introducing an exponential function of the innovation vector to adjust a covariance matrix so as to improve the GCL-UKF-based state estimation accuracy under the change of gain matrix caused by bad factors. Numerical simulation results carried out on IEEE 14-bus, 30-bus, and 57-bus test systems validate the efficacy of the proposed methods for state estimation under various types of measurement. [ABSTRACT FROM AUTHOR]

This brief focuses on the development of a linear Kalman filtering algorithm when the control input variable is corrupted by noises. The noisy input is considered in the derivation process of the Kalman filter, and an extra term is included in the covariance matrix of the one step error. A bias estimation is naturally generated by the input noise. To reduce the bias, a new cost function of the state estimation error with a regularization term is proposed to obtain the Kalman gain matrix. Simulation results in the context of discrete time state estimation demonstrate that the proposed algorithm can achieve excellent estimation performance in terms of the steady-state misalignment under noisy input environments. [ABSTRACT FROM AUTHOR]

This brief proposes a bias-compensated proportionate normalized least mean square (BCPNLMS) method for identifying sparse system when subjected to the noisy input. The proposed BCPNLMS algorithm, which combines the proportionate scheme and the unbiasedness criterion, is able to identify the system parameters with better steady-state accuracy and faster convergence speed than conventional NLMS, bias-compensated NLMS, and PNLMS algorithms. Robustness and high identification accuracy with noisy input can be achieved by introducing the bias-compensation term derived from the unbiasedness criterion. Simulation results on sparse system identification confirm the excellent performance of the proposed BCPNLMS in the presence of both input and output noises. [ABSTRACT FROM AUTHOR]

The global ocean net primary production (NPP) was estimated from satellite-derived information using a phytoplankton pigment absorption (a{\bf ph})-based model. Satellite-derived spectral-averaged a{\bf ph} was used as the key predictor of phytoplankton photosynthetic efficiency in the model. The a{\bf ph}-based model yields an annual integrated NPP of approximately \bf 55~\Pg C year^-1 for the global oceans over the period 2003–2010. The accuracy of the model was validated by comparing it with in situ NPP at three sites, and the logarithmic root-mean-square error of the model was approximately 0.18. The model performance was also compared with two existing NPP models (chlorophyll-based model and carbon-based model) in terms of spatial distribution, seasonal cycles, and accuracy. The comparison results indicated that the a{\bf ph}-based model has improved accuracy in describing NPP variation for monthly timescales compared with the other two models. We were surprised to find that the spatial distribution of global ocean NPP provided by the a{\bf ph}-based model is more similar to the carbon-based model than the chlorophyll-based model. Although many additional studies need to be conducted, the performance of the a{\bf ph}-based model in this work may encourage us to estimate ocean NPP from satellite-derived phytoplankton pigment absorption. [ABSTRACT FROM PUBLISHER]